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Friday, December 18, 2015

An Architecture of Data Science

Nicely done piece by Vincent Granville, about the 'architectural' considerations of data science.  In roughly the order you should progress, but understanding you may have to revisit many elements more than once.   Mostly nontechnical.  This is useful for the entire spectrum of project participants, from scientists to decision makers.  A template for understanding?

" ... In this article, I summarize the components of any data science / machine learning / statistical project, as well as the cross-dependencies between these components. This will give you a general idea of what a data science or other analytic project is about. .... " 

In comparison, related architecture, see also CRISP-DM.

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